Thank you!

13. The Larry Bird free throw shooting data discussed in Section 1.2 can be examined in a logistic regression context. The purpose here is to estimate the probability of success for the second free throw attempt given what happened on the rst free throw attempt. Complete the following: (a) The c.tab1e object created in Bird.R contains the contingency table format of the data. To create the data frame format needed for glmO1 we can re-enter the data in a new data frame with > bird bird First success trials 1 made 251 285 2 missed 48 53 or transform it directly with > birdl (- as.data-frame(as.table(c-table)) > trials success bird2 bird2 First success trials 1 made 251 285 2 missed 48 53 The second method is more general and can work with larger contingency tables like those examined in Chapters 3 and 4. Implement each line of code in the second method above and describe what occurs. Estimate a logistic regression model for the probability of success on the second attempt, where First is the explanatory variable. Estimate the odds ratio comparing the two levels of First. Calculate both Wald and prole LR intervals for the odds ratio. Compare those calculated values with those obtained in the Larry Bird example of Section 1.2.5. If you use conf into to compute the intervals, make sure to use the correct value for the parm argument [same name as the indicator variable given by summary (mod.fit) }. Perform a hypothesis test of Ho : ,61 = 0 vs. H.1 : ,8] 9'5 0 using Wald and LR statistics. Compare these results to those found for the Larry Bird example in Section 1 2.3. Discuss why similarities and/or differences occur between the calculations here using logistic regression and the corresponding calculations in Chapter 1